A subspace approach for restoring image corrupted by white noise

A new subspace approach is proposed for enhancement of image corrupted by additive white noise. In subspace filtering methods, the noisy image is decomposed into two orthogonal subspaces, a signal subspace and a noise subspace. This decomposition is possible under the assumption of a low-rank model for image and the availability of an estimate of the noise covariance matrix. It is shown in this paper that the proposed image restoration method performs better than the Wiener filtering and wavelet denoising techniques.